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| Author | : 馬世晟 |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2014 |
| Genre | : Autoregression (Statistics) |
| ISBN | : |
| Author | : Sai-Shing Ma |
| Publisher | : |
| Total Pages | : |
| Release | : 2017-01-26 |
| Genre | : |
| ISBN | : 9781361337936 |
This dissertation, "On the Long Memory Autoregressive Conditional Duration Models" by Sai-shing, Ma, 馬世晟, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In financial markets, transaction durations refer to the duration time between two consecutive trades. It is common that more frequent trades are expected to be followed by shorter durations between consecutive transactions, while less frequent trades are expected to be followed by longer durations. Autoregressive conditional duration (ACD) model was developed to model transaction durations, based on the assumption that the expected average duration is dependent on the past durations. Empirically, transaction durations possess much longer memory than expected. The autocorrelation functions of durations decay slowly and are still significant after a large number of lags. Therefore, the fractionally integrated autoregressive conditional duration (FIACD) model was proposed to model this kind of long memory behavior. The ACD model possesses short memory as the dependence of the past durations will die out exponentially. The FIACD model possesses much longer memory as the dependence of the past durations will decay hyperbolically. However, the modeling result would be misleading if the actual dependence of the past durations decays between exponential rate and hyperbolic rate. Neither of these models can truly reveal the memory properties in this case. This thesis proposes a new duration model, named as the hyperbolic autoregressive conditional duration (HYACD) model, which combines the ACD model and the FIACD model into one. It possesses both short memory and long memory properties and allows the dependence of the past durations to decay between the exponential rate and the hyperbolic rate. It also indicates whether the dependence is close to short memory or long memory. The model is applied to the transaction data of AT&T and McDonald stocks traded on NYSE and statistically positive results are obtained when it is compared to the ACD model and the FIACD model. DOI: 10.5353/th_b5185908 Subjects: Autoregression (Statistics) Time-series analysis
| Author | : Peter M. Robinson |
| Publisher | : Advanced Texts in Econometrics |
| Total Pages | : 396 |
| Release | : 2003 |
| Genre | : Business & Economics |
| ISBN | : 9780199257300 |
Long memory time series are characterized by a strong dependence between distant events.
| Author | : Albert Rex Bergstrom |
| Publisher | : Oxford University Press, USA |
| Total Pages | : 344 |
| Release | : 1990 |
| Genre | : Business & Economics |
| ISBN | : |
| Author | : Gilles Teyssière |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 394 |
| Release | : 2006-09-22 |
| Genre | : Business & Economics |
| ISBN | : 3540346252 |
Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics.
| Author | : Wilfredo Palma |
| Publisher | : John Wiley & Sons |
| Total Pages | : 306 |
| Release | : 2007-04-27 |
| Genre | : Mathematics |
| ISBN | : 0470131454 |
A self-contained, contemporary treatment of the analysis of long-range dependent data Long-Memory Time Series: Theory and Methods provides an overview of the theory and methods developed to deal with long-range dependent data and describes the applications of these methodologies to real-life time series. Systematically organized, it begins with the foundational essentials, proceeds to the analysis of methodological aspects (Estimation Methods, Asymptotic Theory, Heteroskedastic Models, Transformations, Bayesian Methods, and Prediction), and then extends these techniques to more complex data structures. To facilitate understanding, the book: Assumes a basic knowledge of calculus and linear algebra and explains the more advanced statistical and mathematical concepts Features numerous examples that accelerate understanding and illustrate various consequences of the theoretical results Proves all theoretical results (theorems, lemmas, corollaries, etc.) or refers readers to resources with further demonstration Includes detailed analyses of computational aspects related to the implementation of the methodologies described, including algorithm efficiency, arithmetic complexity, CPU times, and more Includes proposed problems at the end of each chapter to help readers solidify their understanding and practice their skills A valuable real-world reference for researchers and practitioners in time series analysis, economerics, finance, and related fields, this book is also excellent for a beginning graduate-level course in long-memory processes or as a supplemental textbook for those studying advanced statistics, mathematics, economics, finance, engineering, or physics. A companion Web site is available for readers to access the S-Plus and R data sets used within the text.
| Author | : Jaya P. N. Bishwal |
| Publisher | : Springer Nature |
| Total Pages | : 634 |
| Release | : 2022-08-06 |
| Genre | : Mathematics |
| ISBN | : 3031038614 |
This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
| Author | : Richard Baillie |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
The presence of long memory in realized volatility () is a widespread stylized fact. The origins of long memory in have been attributed to jumps, structural breaks, contemporaneous aggregation, nonlinearities, or pure long memory. An important development has been the heterogeneous autoregressive () model and its extensions. This article assesses the separate roles of fractionally integrated long memory models, extended models and time varying parameter models. We find that the presence of the long memory parameter is often important in addition to the models.
| Author | : Uwe Hassler |
| Publisher | : John Wiley & Sons |
| Total Pages | : 292 |
| Release | : 2018-09-07 |
| Genre | : Mathematics |
| ISBN | : 1119470285 |
Provides a simple exposition of the basic time series material, and insights into underlying technical aspects and methods of proof Long memory time series are characterized by a strong dependence between distant events. This book introduces readers to the theory and foundations of univariate time series analysis with a focus on long memory and fractional integration, which are embedded into the general framework. It presents the general theory of time series, including some issues that are not treated in other books on time series, such as ergodicity, persistence versus memory, asymptotic properties of the periodogram, and Whittle estimation. Further chapters address the general functional central limit theory, parametric and semiparametric estimation of the long memory parameter, and locally optimal tests. Intuitive and easy to read, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency Domain Analysis; Differencing and Integration; Fractionally Integrated Processes; Sample Means; Parametric Estimators; Semiparametric Estimators; and Testing. It also discusses further topics. This book: Offers beginning-of-chapter examples as well as end-of-chapter technical arguments and proofs Contains many new results on long memory processes which have not appeared in previous and existing textbooks Takes a basic mathematics (Calculus) approach to the topic of time series analysis with long memory Contains 25 illustrative figures as well as lists of notations and acronyms Time Series Analysis with Long Memory in View is an ideal text for first year PhD students, researchers, and practitioners in statistics, econometrics, and any application area that uses time series over a long period. It would also benefit researchers, undergraduates, and practitioners in those areas who require a rigorous introduction to time series analysis.
| Author | : Nikolaus Hautsch |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 297 |
| Release | : 2011-01-07 |
| Genre | : Business & Economics |
| ISBN | : 3642170153 |
This book provides a methodological framework to model univariate and multivariate irregularly spaced financial data. It gives a thorough review of recent developments in the econometric literature, puts forward existing approaches and opens up new directions. The book presents alternative ways to model so-called financial point processes using dynamic duration as well as intensity models and discusses their ability to account for specific features of point process data, like the occurrence of time-varying covariates, censoring mechanisms and multivariate structures. Moreover, it illustrates the use of various types of financial point processes to model financial market activity from different viewpoints and to construct volatility and liquidity measures under explicit consideration of the passing trading time.
